What is the clinical evidence from this study?

Study design: Other. Population: Electrocardiogram (ECG) privacy. Intervention: Three-stage compositional privacy mechanism. Primary outcome: Morphological adversary attack success (MLP AUC 0.671, 95% CI 0.551-0.693).

What does this research mean for the field?

A novel three-stage compositional privacy mechanism combining wavelet-domain Gaussian noise, R-R interval quantization, and feature-layer differential privacy successfully protects bioelectric signals against identification and membership inference attacks while preserving signal utility and real-time performance. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to validate a new three-stage compositional privacy mechanism for bioelectric digital twins using ECG signals.

June 3, 2026Open Access

Compositional Privacy for Real Bioelectric Digital Twins: A 104-Study Pre-Registered Validation on Six ECG Cohorts

Key Result

A three-stage compositional privacy mechanism defeated a capable morphological adversary (MLP AUC 0.671; 95% CI 0.551-0.693) while preserving signal-domain reconstruction (RMSE 0.039 mV).

Key Points

This research aims to validate a new three-stage compositional privacy mechanism for bioelectric digital twins using ECG signals.
Conducted a 104-study pre-registered empirical validation campaign with approximately 520 hypotheses.
Implemented continuous deterministic protocols with zero post-hoc threshold adjustments.
Utilized multiple ECG cohorts from PhysioNet for cross-dataset validation.
Phase XXII frequency-separation principle did not suppress ECG identity content.
The three-stage mechanism achieved ensemble AUC = 0.583 and preserved signal-domain reconstruction (RMSE = 0.039 mV).
Cross-dataset validation succeeded on five of six ECG cohorts, with the sixth cohort requiring tuning.

Structured PICO

Population

Six real PhysioNet ECG cohorts (MIT-BIH 100-series, MIT-BIH Long-Term, MIT-BIH Normal Sinus Rhythm, PTB-XL clinical 12-lead, Apnea-ECG, MIT-BIH 200-series arrhythmia cohort)

Intervention

Three-stage compositional privacy mechanism (wavelet-domain Gaussian noise on Daubechies-4 D2–D4 detail subbands with σ = 0.3, R-R interval quantization with Δ = 1.0 s, and feature-layer differential privacy on the morphology subset with ε = 5 and δ = 10⁻⁵)

Outcome

Defeating morphological adversary (AUC) while preserving signal-domain reconstruction (RMSE) and real-time deployment characteristics

A novel three-stage compositional privacy mechanism successfully protects ECG data against morphological adversaries while maintaining signal reconstruction quality and real-time deployment capabilities.

Main Result

Effect estimate: MLP AUC 0.671 (95% CI 0.551-0.693)

Limitations

multi-session vulnerability
auxiliary-information linkage
Z-domain attacker
per-patient fairness gap
two failed naive fixes
MIT-BIH 200-series arrhythmia cohort requires per-cohort σ tuning

Abstract

This deposit contains the complete pre-registration corpus, runner code, summary verdicts, per-study reports, figures, POC addenda, master synthesis, and arXiv-format manuscript for a 104-study pre-registered empirical validation campaign of a novel three-stage compositional privacy mechanism for real bioelectric digital-twin deployments (electrocardiogram, with synthetic and real EEG extensions). The campaign comprises approximately 520 pre-registered hypotheses across seven thematic batches, executed under continuous deterministic protocol (PYTHONHASHSEED=0) with zero post-hoc threshold adjustments. Pre-registered hypothesis verdicts are honestly reported; bounded-negative findings are explicitly preserved as deployment-boundary characterizations rather than suppressed. The campaign extends the companion Paper 8 (10.5281/zenodo.20466035) and Paper 9 (10.5281/zenodo.20470763) from articulated mechanical systems (musculoskeletal kinematics, surgical-robot patient-pair state) to bioelectric signals. Key empirical findings: (i) the Phase XXII frequency-separation principle does *not* transfer to bioelectric — notch filtering does not selectively suppress ECG identity content; (ii) a discovered three-stage compositional mechanism (wavelet-domain Gaussian noise on Daubechies-4 D2–D4 detail subbands with σ = 0.3, R-R interval quantization with Δ = 1.0 s, and feature-layer differential privacy on the morphology subset with ε = 5 and δ = 10⁻⁵) defeats a capable morphological adversary (ensemble AUC = 0.583, MLP AUC = 0.671, bootstrap 95% CI 0.551, 0.693) while preserving signal-domain reconstruction (RMSE = 0.039 mV) and real-time deployment characteristics (0.4 ms per-trial latency, 2,370 trials/sec); (iii) the mechanism defeats four standard attack model families (identification, membership inference AUC = 0.576, set-membership / PSI AUC = 0.557, reconstruction); (iv) cross-dataset validation succeeds on five of six real PhysioNet ECG cohorts (MIT-BIH 100-series, MIT-BIH Long-Term, MIT-BIH Normal Sinus Rhythm, PTB-XL clinical 12-lead, Apnea-ECG), with the sixth (MIT-BIH 200-series arrhythmia cohort, AUC = 0.843) honestly disclosed as Limitation 11 requiring per-cohort σ tuning; (v) eleven explicit limitations are catalogued including multi-session vulnerability, auxiliary-information linkage, Z-domain attacker, per-patient fairness gap, and two failed naive fixes. The deposit includes 59 pre-registration files (Studies 201-259, with Studies 260-300 carrying inline pre-registrations in their runner scripts), 103 runner Python scripts, 104 JSON summary verdicts, 101 per-study reports, 8 figures, a comprehensive POC summary document (104 detailed per-study addenda with full hypothesis tables), the Phase XXIII master synthesis, and the v3 polished manuscript (markdown + two-column IEEE arXiv-format Word and PDF). All studies reproduce bit-identically under PYTHONHASHSEED=0 with the pinned numpy 1.26.x / scipy 1.11.x / scikit-learn 1.4.x / PyWavelets 1.5.x / wfdb 4.1.x / pandas 2.1.x dependency stack. All input data are publicly available from PhysioNet (https://physionet.org) under the ODC-By license. Released under CC-BY 4.0. v3.1 corrigendum--corrected licensing-inquiry email from rferlic@fieldstoneanalytics.com to randolphf@fieldstoneanalyticsllc.com. No scientific content changed.